Regional clozapine, ECT and lithium usage inversely associated with excess suicide rates in male adolescents

Advanced psychiatric treatments remain uncertain in preventing suicide among adolescents. Across the 21 Swedish regions, using nationwide registers between 2016–2020, we found negative correlation between adolescent excess suicide mortality (AESM) and regional frequencies of clozapine, ECT, and lithium (CEL) usage among adolescents (β = −0.613, p = 0.0003, 95% CI: −0.338, −0.889) and males (β = −0.404, p = 0.009, 95% CI: −0.130, −0.678). No correlation was found among females (p = 0.197). Highest CEL usage among male adolescents was seen in regions with lowest quartile (Q1) AESM (W = 74, p = 0.012). Regional CEL treatment frequency in 15–19-year-olds was related to lower AESM in males, reflecting potential treatment efficacy, treatment compliance or better-quality mental health care. Suicide prevention may benefit from early recognition and CEL treatment for severe mental illness in male adolescents. The results indicate association but further research, using independent samples and both prospective and observational methodologies, is needed to confirm causality.


Statistical considerations
Prior to statistical association analyses, several measures were taken with the aim of strengthening robustness of included variables. These initial steps were performed in using Microsoft Excel for Microsoft 365 MSO (Version 2204 Build 16.0.15128.20278). (1) Initially, the mean and standard deviation was compared for each key variable across 2016 to 2020 in both sexes, males, and femalesmeasured per 100,000 inhabitants. Several counties exhibited greater standard deviation than mean value for key vari ables, i.e., for clozapine (both sexes, n=9; females, n=9; males, n=9), lithium (both sexes, n=2; females, n=4; males, n=5), ECT (both sexes, n=15; females, n=9; males, n=8), baseline adolescent suicide rates (both sexes, n=7; females, n=11; males, n=10) and baseline suicide rates in young adulthood (both sexes, n=2; females, n=13; males, n=5)overall implicating that the median value across 2016 to 2020 would be more statistically appropriate as compared to the mean. However, it was noted that several counties provided certain treatments or exhibited adolescent suicide deaths only for one to two of the five years i.e., for clozapine (both sexes, n=4; females, n=4; males, n=6), lithium (both sexes, n=0; females, n=1; males, n=3), ECT (both sexes, n=9; females, n=6; males, n=5), baseline adolescent suicide rates (both sexes, n=5 females, n=10; males, n=8) and baseline adult suicide rates (both sexes, n=1; females, n=11; males, n=2). Hence, both mean and median values were considered inappropriate to adequately represent treatment usage frequencies and suicide death rates across all counties in the years 2016-2020. Therefore, min-max normalization was implemented across each year, region, and treatment type (i.e., for example, DerivedValue2016 = Value2016-min(Value2016-2020)/(max(Value2016-2020)-min(Value2016-2020)). In the case of suicide death rates, the difference in suicide rates in adolescence and young adulthood per 100,000 inhabitants was calculated for each region, year and sex group (i.e., DerivedExcessSuicidRateAdolescence2016 = SuicideRateAdolescence2016 -SuicideRateYoungAdulthood2016)). Thus, one excess-suicide-rate value was derived for each region, year, and sex group (both sexes, females, and males, respectively). Subsequently, this value was subjected to the same min-max normalization procedure as described above. In cases of zero-value denominators, derived values were manually inserted as zero. The average min-max normalized values across 2016-2020 was implemented in the subsequent analysis, whereby each region and sex-group was represented by a mean treatment value measured across 2016-2020 and one value for excess adolescent suicide deaths. When compared to the median derived values across 2016-2020, the mean value was considered more representative of the underlying data distribution by, for example, also recognizing counties providing treatment in only one or two years in the studied five-year-period. Derived values were visually inspected across all key variables and sex-groups and deemed overall representative of the underlying data (Supplementary Table 1.). (2) To reduce effects of potential unmeasured sources of confound on suicide rates across counties, the primary outcome variables were based on subtraction between suicide death rated in adolescence and young adulthood. Thus, the influence of regional differences on suicide death rates in, for example, population size, socioeconomic status, substance abuse, or availability and quality of psychiatric care, could thus largely be reduced. Similarly, subsequent min-max normalization contributed to normalizing potential confounding effects of substantial outliers regarding baseline absolute values. (3) To ensure measurements for lithium and clozapine correspond to long-term usage, extracted data consisted of the number of dispensations. Thus, any patients receiving treatment for a short-time period (for example during inpatient treatment)arguably not reflective of adequate treatment -would exert less influence compared to patients consistently provided with such prescriptions. This was not possible in the case of ECT, as the number of ECT treatments received was not openly available. Thus, instead the total number of patients receiving ECT treatment was implemented. Moreover, large fluctuations in advanced treatment usage were not considered conciliable with best practice care. Especially so in the case of lithium and clozapine where, for example, dramatic reductions in the number of dispensations (sometimes to zero) could indicate that all treated patients either transitioned into a different age-group or, more probably, conferred by a substantial portion of patients being discontinued from such advanced treatments. Thus, min-max normalization contributed to downregulating potentially confounding treatment values from regions exhibiting one or two years with extremely high values and zero (no treatment) for the other years. (4). The timeperiod was chosen to match the major course direction in national treatment guidelines regarding ECTsince 2016 and with highest priority recommending its use in the care of post-pubertal adolescents with severe MDD with mood-congruent psychotic symptoms, catatonia, or treatment resistance. Similarly, information on regional suicide death rates was available up to 2020, hence, 2021-2022 treatment frequencies were not included in the analysis (5). Moreover, confounding could arise from the putative general underutilization of ECT for minorsi.e., according to the Swedish National Quality Registry for ECT (coverage rate ~90%) fewer than 20 adolescents in Sweden received ECT treatment in 2020 1 , encompassing a population exceeding 10 million. Aversiveness to brain stimulation techniques could thus mask the adequate recognition of severe mental illness, in the case of clinicians adequately implementing lithium and clozapine on correct indication but withholding the option of ECT-treatment from adolescent populations. Thus, mean usage frequencies across the min-max normalized 2016-2020 values (derived in Section 2.1 (1) was implemented for the subsequent analysis in the case of ECT (ages 13-17), lithium and clozapine (both ages 15-19). These measures should further increase robustness, while also allowing for the recognition of ECT-aversive (or Clozapine or Lithiumaversive) counties that may provide adolescents with adequate recognition of severe mental illness (and treatment with other studied modalities when indicated). (7) Adolescent treatment usage frequencies were generally of small magnitude across counties. Hence, it cannot be completely excluded that the influence of a single clinician in a medium-sized region would significantly alter measured frequencieswhile possibly unrepresentative of the care provided in the full region. To reduce the influence of any outlier counties regarding frequency of treatment usage, we implemented robust linear regression models.
Lastly, (8) we recognized the large impact that a single suicide attempt or ECT-treatment could yield in a small to medium-sized region, potentially exerting disproportionate confounding effects on downstream analyses. Therefore, additional weights were added to the robust models based on regional population in relation to total population (2020 estimates)reducing potential unbalanced effects from outlier counties with small populations.

Statistical analysis
Initial data processing (i.e., calculation of min-max normalized values) was performed using Microsoft Excel365 MSO [Version 2210 Build 16.0.15726.20188] 64-bit). All downstream statistical analyzes were performed using R version 4.0.3. The variables included in the analysis pertained to mean usage frequencies across the min-max normalized 2016-2020 values for ECT (ages 13-17), lithium and clozapine (both ages 15-19); and the min-max normalized differential suicide death rate between adolescence and young adulthood. A total of 21 Swedish counties were includedeach with three proxy values for clozapine-ECTlithium usage frequencies (both sexes, females, and males, respectively) and three proxy values for excess adolescent suicide rates (both sexes, females, and males, respectively). The Shapiro-Wilk normality test indicated that all included (six) variables satisfied requirements for normal distribution (p>0.1) (Supplementary Figure 1.). The relative contribution of each treatment modality to the averaged treatment proxy variable were assessed by comparing coefficients by multiple ordinary least squares regression (Proxy2016-2020~Clozapine2016-2020+ECT2016-2020+Lithium2016-2020)performed separately for each sex category (both sexes, males, and females, respectively). Associations across counties between excess suicide death rates in adolescence and mean treatment usage frequencies by robust linear regression models using the R-packages 'robustbase' 2 and 'rcompanion' 3 , specifying recommended setting (KS2014)performed separately for both sexes, males and females, respectively 4 . Chain of regression estimates included the standard MM-regression estimator (guaranteeing an acceptable compromise between high breakdown (i.e., 50%) and very high efficiency (i.e., 95%) 5 )while adjusting for weights (2020 regional population in proportion to national estimates the same year) and using the recommended setting (KS2014). Models exhibiting p-values for the primary explanatory variable < 0.05 were considered significant. Main models were illustrated by x-y scatterplots using the R-package 'ggplot2' 6 , with the estimated slope coefficient from the regression model (Figure 1.). As a final validation step of significant models, we tested the hypothesis whether counties in the lower quartile (Q1) regarding excess adolescent suicide deaths exhibited greater advanced treatment usage frequencies in adolescents compared to Q2-Q4 counties. The excess adolescent suicide death rate variable was thus dichotomized by the 25 th quartile and contrasted to the estimate of advanced treatment usage frequencies by the one-sided Wilcoxon rank sum exact test. P-values < 0.05 were considered significant and these models were illustrated by dotplot boxplots using the R-package 'ggplot2' 6 . An openly available code originally posted by Laura DeCicco was further implemented to provide a more detailed complementary legend plot 7 . Post-hoc analyses were subsequently performed to determine the effects of any single treatment modality. First, the min-max normalized values across 2016-2020 for clozapine, ECT and lithium, respectively, were assessed by the Shapiro Wilk's testindicating non-normal distribution of values for clozapine and ECT across all investigated sex groups (p<0.05), thus subjected to transformation by Blom's method 8 for subsequent robust linear regression analyses (but not for nonparametric tests, i.e., the Wilcoxon rank sum exact test). To minimize putative bias from collinearity on downstream analyzes, the mean of treatment variables that were strongly correlated according to the Pearson's Correlation Coefficient (i.e., r>0.5) was calculated, tested for normal distribution by Shapiro Wilk's tests and implemented in subsequent analyses (i.e., ECT and lithium for the combined sex group, and clozapine and lithium for the male subgroup). Thereafter, robust linear regression models (same specifications as in the main model) were implemented, contrasting excess adolescent suicide death rates to these variables, separately for each sex group (i.e., for example -SuicideRates2016-2020~Clozapine2016-2020+Mean(ECT2016-2020 + Lithium2016-2020) -for the combined sexes group). To reduce potential bias from overfitting of the model (given the small sample size, n=21 regions), treatment variables were tested separately for the female subgroupand resulting significance values were subjected to stringent Bonferroni- Abbreviations: ECT, electroconvulsive therapy; Q1, first quartile; Q2-Q4, second, third and fourth quartile.

Supplementary Figure 3. ECT Usage Frequencies and Regional Excess Suicide Deaths in Females (2016-2020)
Figure legend: The Y-axis depicts the regional mean of min-max normalized year-wise differences in female suicide deaths per 100,000 inhabitants between adolescence and young adulthood (variables derived in Section 2.1 (1). The X-axis depicts regional mean values of min-max normalized ECT treatment usage frequencies in females across 2016-2020 (derived in Section 2.1 (1)) -subsequently transformed for normalization purposes by Blom's method. The slope and confidence intervals (CI:s) of the robust linear regression model contrasting these two variables are depicted as a blue line (slope) with grey shading (CI:s). Region population in relation to the national population are illustrated by the circle diameter and counties affiliated with medical Universities are highlighted in blue (i.e., Skåne region -Lund University, Stockholm region -Karolinska Institutet, Uppsala region -Uppsala University, Västerbotten region -Umeå University, Västra Götaland region -Gothenburg University/Sahlgrenska Academy). The figures demonstrate that ECT usage frequencies across 2016-2020 are inversely correlated with regional excess adolescent suicide deaths in females (β = -0.613, p-value=0.005, Bonferroni-adjusted p-value=0.016, multiple R-squared: 0.148, adjusted R-squared: 0.104, 95% CI: -0.014, -0.062).
Abbreviations: 95% CI, 95% confidence interval; ECT, electroconvulsive therapy; U.Affil, medical university affiliation (regions affiliated to medical universities are coloured in blue and regions unaffiliated to medical universities are coloured in black); W, weights (regional population size expressed as a percentage of the total national population).

Deaths in Females and Males, respectively (2016-2020)
Figure legend: The Y-axis depicts regional values of min-max normalized ECT treatment usage frequencies averaged across 2016-2020 (derived in Section 2.1 (1)) in females and males, respectively. The X-axis depicts the lower (Q1) and other (Q2-Q4) counties regarding mean of min-max normalized year-wise differences in suicide deaths per 100,000 inhabitants between adolescence and young adulthood (variables derived in Section 2.1 (1)illustrating values for females and males, respectively. Thus, per definition, Q1-counties exhibited lower excess adolescent suicide deaths in comparison to Q2-Q4. Treatment usage frequencies were compared between the in-silico generated subgroups in using the one-sided Wilcoxon rank sum exact test. Lower-quartile counties regarding excess adolescent suicide deaths were associated with higher mean treatment usage frequencies of ECT in females (W=67, p=0.039; Q1 -minima: 0,